@inproceedings{c4934d3dac4b411d94de76fbfa163cb5,
title = "Demand Modeling of a dc Fast Charging Station",
abstract = "This paper presents the modeling and simulation of power loads due to plug-in electric vehicles' (EVs) charging events at a dc fast charging station. Two algorithms for modeling the loads are introduced and compared, one based on sampling and one based on statistical distribution built from the sample database. Simulation of load versus time was performed using a horizon of 7 days using both techniques. The cause for the difference in the result of these two approaches is explored. Regardless of the method used, the results show that a dc fast charging station with 6 fast chargers potentially serving 700 plugin EVs generally gets 105 charging events per day with a peak load of 375 kW.",
keywords = "Dc fast charging station, Demand forecasting, Electric vehicles, Load modeling",
author = "Qian Deng and Sujit Tripathy and Daniel Tylavsky and Travis Stowers and Jeff Loehr",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 North American Power Symposium, NAPS 2018 ; Conference date: 09-09-2018 Through 11-09-2018",
year = "2019",
month = jan,
day = "2",
doi = "10.1109/NAPS.2018.8600618",
language = "English (US)",
series = "2018 North American Power Symposium, NAPS 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 North American Power Symposium, NAPS 2018",
}